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qdrant-search-quality

qdrant
Updated 6 days ago
158
18
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Metadesigndata

About

This skill helps developers diagnose and improve search relevance in Qdrant vector databases. It addresses issues like poor results, low precision/recall, and guides on embedding models, hybrid search, and reranking. Use it when search quality degrades or when needing to measure retrieval performance with techniques like building ground truth datasets.

Quick Install

Claude Code

Recommended
Primary
npx skills add qdrant/skills -a claude-code
Plugin CommandAlternative
/plugin add https://github.com/qdrant/skills
Git CloneAlternative
git clone https://github.com/qdrant/skills.git ~/.claude/skills/qdrant-search-quality

Copy and paste this command in Claude Code to install this skill

Documentation

Qdrant Search Quality

First determine whether the problem is the embedding model, Qdrant configuration, or the query strategy. Most quality issues come from the model or data, not from Qdrant itself. If search quality is low, inspect how chunks are being passed to Qdrant before tuning any parameters. Splitting mid-sentence can drop quality 30-40%.

  • Start by testing with exact search to isolate the problem Search API

Diagnosis and Tuning

Isolate the source of quality issues, establish labeled baselines to measure recall and relevance, tune HNSW parameters, and choose the right embedding model. Diagnosis and Tuning

Search Strategies

Hybrid search, reranking, relevance feedback, and exploration APIs for improving result quality. Search Strategies

GitHub Repository

qdrant/skills
Path: skills/qdrant-search-quality
0
agent-skillsai-agentsclaude-codecodexcursorembeddings

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